Methods and smart gas internet of things (iot) systems for remote control of ultrasonic metering devices

ABSTRACT

Embodiments of the present disclosure provide a method and a smart gas Internet of Things (IoT) system for remote control of an ultrasonic metering device. The method is implemented based on a smart gas device management platform of the IoT system and comprises: obtaining metering data of at least one ultrasonic metering device; determining any one of the at least one ultrasonic metering device as a current metering device; determining an accuracy of the current metering device through verifying the current metering device based on metering data of the current metering device and metering data of a related metering device; and sending an adjustment instruction to a target metering device based on the accuracy of at least one current metering device corresponding to the at least one ultrasonic metering device.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the priority of the Chinese Patent ApplicationNo. 202310834657.7, filed on Jul. 10, 2023, the entire contents of whichare incorporated herein by reference.

TECHNICAL FIELD

The present disclosure relates to the field of Internet of Thingstechnology, and in particular to a method and a smart gas Internet ofThings (IoT) system for remote control of an ultrasonic metering device.

BACKGROUND

With the widespread use of gas in life, ultrasonic metering devices areincreasingly used in gas metering management. A plurality of ultrasonicmetering devices installed in a gas pipeline may measure and monitor agas flow rate in the gas pipeline. However, for ultrasonic meteringdevices, there are still problems in terms of lagging remote control,low intelligence, and low management efficiency of the accuracy ofultrasonic metering devices.

Therefore, it is desired to provide a method and a smart gas Internet ofThings (IoT) system for remote control of an ultrasonic metering device,which may remotely control the ultrasonic metering device and improvethe management efficiency and intelligence of the accuracy of theultrasonic metering device.

SUMMARY

One of the embodiments of the present disclosure provides a method forremote control of an ultrasonic metering device, wherein the method isimplemented by a smart gas device management platform of an Internet ofThings (IoT) system for remote control of the ultrasonic meteringdevice, and the method includes: obtaining metering data of at least oneultrasonic metering device; determining any one of the at least oneultrasonic metering device as a current metering device; determining anaccuracy of the current metering device through verifying currentmetering device based on the metering data of the current meteringdevice and metering data of a related metering device, wherein therelated metering device includes at least one of an upper meteringdevice, a lower metering device, and a parallel metering device of thecurrent metering device; and sending an adjustment instruction to atarget metering device based on the accuracy of at least one currentmetering device corresponding to the at least one ultrasonic meteringdevice.

One of the embodiments of the present disclosure provides an Internet ofThings (IoT) system for remote control of an ultrasonic metering device,wherein the IoT system includes a smart gas user platform, a smart gasservice platform, a smart gas device management platform, a smart gassensing network platform, and a smart gas object platform, wherein thesmart gas user platform includes a plurality of smart gas usersub-platforms; the smart gas service platform includes a plurality ofsmart gas service sub-platforms; the smart gas device managementplatform includes a plurality of smart gas device sub-platforms and asmart gas data center, the smart gas device management platform beingconfigured to transmit an adjustment instruction to the smart gassensing network platform via the smart gas data center; the smart gassensing network platform is configured to interact with the smart gasdata center and the smart gas object platform, and send the adjustmentinstruction to the smart gas object platform; the smart gas objectplatform is configured to obtain metering data of at least oneultrasonic metering device; the smart gas device management platform isconfigured to: determine any one of the at least one ultrasonic meteringdevice as a current metering device; determine an accuracy of thecurrent metering device through verifying the current metering devicebased on the metering data of the current metering device and meteringdata of a related metering device, wherein the related metering deviceincludes at least one of an upper metering device, a lower meteringdevice, and a parallel metering device of the current metering device;and send the adjustment instruction to a target metering device based onthe accuracy of at least one current metering device corresponding tothe at least one ultrasonic metering device.

One of the embodiments of the present disclosure provides anon-transitory computer-readable storage medium, wherein the storagemedium stores computer instructions, and when a computer reads thecomputer instructions in the storage medium, the computer implements themethod for remote control of the ultrasonic metering device.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure will be further illustrated by way of exemplaryembodiments, which will be described in detail by way of theaccompanying drawings. These embodiments are not limiting, and in theseembodiments the same numbering indicates the same structure, wherein:

FIG. 1 is a diagram of a structure of platforms of an Internet of Things(IoT) system for remote control of an ultrasonic metering deviceaccording to some embodiments of the present disclosure;

FIG. 2 is an exemplary flowchart of a method for remote control of theultrasonic metering device according to some embodiments of the presentdisclosure;

FIG. 3 is an exemplary flowchart of determining an upload frequencyinstruction according to some embodiments of the present disclosure; and

FIG. 4 is an exemplary schematic diagram of determining a suspiciousabnormal parameter according to some embodiments of the presentdisclosure.

DETAILED DESCRIPTION

In order to more clearly illustrate the technical solutions of theembodiments of the present disclosure, the following will be a briefdescription of the accompanying drawings that need to be used in thedescription of the embodiments. It will be apparent that theaccompanying drawings in the following description are only examples orembodiments of the present disclosure, and that other similar scenariosmay be applied to the present disclosure by those of ordinary skill inthe art, without creative effort. Unless obviously obtained from thecontext or the context illustrates otherwise, the same numeral in thedrawings refers to the same structure or operation.

Flowcharts are used throughout the present disclosure to illustrate theoperations performed by the system according to embodiments of thepresent disclosure. It should be understood that the preceding orfollowing operations are not necessarily performed in exact order.Instead, the individual steps may be processed in reverse order orsimultaneously. It is also possible to add other operations to theseprocedures or remove a step or steps from them.

Some embodiments of the present disclosure evaluate accuracies of theultrasonic metering devices based on the received metering data of aplurality of ultrasonic metering devices; and send control instructions(such as a data upload frequency and a parameter adjustment instruction)to the metering devices according to the accuracies of the meteringdevices, enabling remote control of the ultrasonic metering devices.

FIG. 1 is a diagram of a structure of platforms of an Internet of Things(IoT) system for remote control of an ultrasonic metering deviceaccording to some embodiments of the present disclosure.

As shown in FIG. 1 , an IoT system 100 for remote control of a meteringdevice may include a smart gas user platform 110, a smart gas serviceplatform 120, a smart gas device management platform 130, a smart gassensing network platform 140, and a smart gas object platform 150connected in sequence.

The smart gas user platform may be a platform configured to interactwith users. In some embodiments, the smart gas user platform may beconfigured as a terminal device.

In some embodiments, the smart gas user platform may include a pluralityof smart gas user sub-platforms, for example, a gas user sub-platform, asupervision user sub-platform, and a government user sub-platform.

The gas user sub-platform may be configured to provide gas users withrelevant data on gas usage and solutions to gas problems. Thesupervision user sub-platform may be configured to supervise operationsof the entire IoT system 100 for remote control of the ultrasonicmetering device. The government user sub-platform may be a platform thatprovides government users with data related to gas operations.

In some embodiments, the smart gas user platform may send gas deviceparameter management information (e.g., metering data of the meteringdevice, etc.) to a gas user through the gas user sub-platform. Detailsabout the metering data of the metering device may be found in FIG. 2and its related contents.

The smart gas service platform may be a platform for receiving andtransmitting data and/or information such as advisory information, queryinstructions, troubleshooting solutions, etc. The smart gas serviceplatform may obtain the gas device parameter management information,etc. from a smart gas safety management platform (e.g., a smart gas datacenter) and send the gas device parameter management information to thesmart gas user platform.

In some embodiments, the smart gas service platform may include aplurality of smart gas service sub-platforms, for example, a smart gasconsumption service sub-platform, a smart supervision servicesub-platform, and a smart operation service sub-platform. Differentsmart gas service sub-platforms correspond to and interact withdifferent smart gas user sub-platforms.

The smart gas service sub-platform may be a platform that provides gasservices for the gas users.

The smart supervision service sub-platform may be a platform thatprovides safety supervision service needs for supervisory users.

The smart operation service sub-platform may be a platform that providesthe government users with relevant information on gas operations.

In some embodiments, the smart gas service platform may send themetering device parameter management information to the government usersub-platform based on the smart operation service sub-platform.

The smart gas device management platform refers to a platform thatoverall plans and coordinates connections and collaborations amongvarious functional platforms, converges all information of the IoTsystem, and provides perception management and control managementfunctions for operations of the IoT system.

In some embodiments, the smart gas device management platform mayinclude the smart gas data center and a plurality of smart gas devicemanagement sub-platforms, for example, a smart gas indoor deviceparameter management sub-platform and a smart gas pipeline networkdevice parameter management sub-platform.

The smart gas indoor device parameter management sub-platform may be aplatform for managing smart gas indoor devices. In some embodiments, thesmart gas indoor device parameter management sub-platform may includebut is not limited to, a device operation parameter monitoring andwarning module and a device parameter remote management module. Thesmart gas indoor device parameter management sub-platform may analyzeand process data related to smart gas indoor devices through theaforementioned modules.

The smart gas pipeline network device parameter management sub-platformmay be a platform configured to manage the smart gas pipeline networkdevice. In some embodiments, the smart gas pipeline network deviceparameter management sub-platform may include but is not limited to, adevice operation parameter monitoring and warning module and a deviceparameter remote management module. The smart gas pipeline networkdevice parameter management sub-platform may analyze and process datarelated to the smart gas pipeline network device through theaforementioned modules.

The smart gas data center may be configured to store and manage alloperation information of the IoT system 100 for remote control of theultrasonic metering device. In some embodiments, the smart gas datacenter may be configured as a storage device for storing related data ofparameter management of indoor device and pipeline network device, etc.For example, the related data may be management data of parameters onmetering device, including monitoring data on the operation of themetering device in the gas pipeline.

In some embodiments, the smart gas device management platform maydetermine any one of at least one ultrasonic metering device as acurrent metering device; determine an accuracy of the current meteringdevice through verifying the current metering device based on themetering data of the current metering device and metering data of arelated metering device; send an adjustment instruction to a targetmetering device based on the accuracy of at least one current meteringdevice corresponding to the at least one ultrasonic metering device; andupload the adjustment instruction to the smart gas sensing networkplatform through the smart gas data center. More about the above sectionmay be found in FIG. 2 and its related description.

In some embodiments, the smart gas device management platform mayinteract with the smart gas service platform and the smart gas sensingnetwork platform through the smart gas data center for informationinteraction, respectively. For example, the smart gas data center maysend the related data of the parameter management of the indoor deviceand pipeline network device to the smart gas service platform. Asanother example, the smart gas data center may send instructions to thesmart gas sensing network platform to obtain the related data of theparameter management of the indoor device and pipeline network device.

The smart gas sensing network platform may be a functional platform formanaging sensing communication. In some embodiments, the smart gassensing network platform may perform functions of sensing informationsensing communication and control information sensing communication.

In some embodiments, the smart gas sensing network platform may includea smart gas indoor device sensing network sub-platform and a smart gaspipeline network device sensing network sub-platform, which may beconfigured to obtain operation information of a gas indoor device and agas pipeline network device, respectively.

In some embodiments, the smart gas sensing network platform may interactwith the smart gas data center and the smart gas object platform. Forexample, the smart gas sensing network platform transmits adjustmentinstructions to the smart gas object platform.

The smart gas object platform may be a functional platform forgenerating sensing information and executing control information. Forexample, the smart gas object platform monitors and generates theoperation information of the gas pipeline network device.

In some embodiments, the smart gas object platform may be configured toobtain the metering data of the at least one ultrasonic metering device.

In some embodiments, the smart gas object platform may include a smartgas indoor device object sub-platform and a smart gas pipeline networkdevice object sub-platform.

In some embodiments, the smart gas indoor device object sub-platform maybe configured as various types of gas indoor devices (e.g., ultrasonicgas meters, ultrasonic flow meters, etc.) for the gas users.

In some embodiments, the smart gas pipeline network device objectsub-platform may be configured as various types of gas pipeline networkdevices and monitoring devices. For example, the pipeline networkdevices may include gas pipelines, pressure regulating stations, etc.,of the pipeline network, and the monitoring devices may include flowmeters configured in the gas pipeline, etc.

Based on the IoT system 100 for remote control of the ultrasonicmetering device, a closed loop of information operation may be formedbetween the smart gas object platform and the smart gas user platform,facilitating coordinated and regulated operations under unifiedmanagement of the IoT system 100 for remote control of the ultrasonicmetering device, thus realizing digitized and intelligent parametermanagement of the indoor device and pipeline network device.

FIG. 2 is an exemplary flowchart of a method for remote control of theultrasonic metering device according to some embodiments of the presentdisclosure. In some embodiments, a process 200 may be implemented basedon a smart gas device management platform. As shown in FIG. 2 , theprocess 200 includes the following steps.

Step 210, obtaining metering data of at least one ultrasonic meteringdevice.

The at least one ultrasonic metering device may be provided at one ormore locations (e.g., at a plurality of gas inlets) in a gas pipeline.The gas inlet of the gas pipeline may include a main gas pipeline inlet,a branch gas pipeline inlet, etc.

The metering data refers to data that characterizes a gas flow in thegas pipeline.

In some embodiments, the metering data may be obtained by monitoring thegas pipeline with the ultrasonic metering devices. The smart gas devicemanagement platform may obtain the metering data of different locationsof the gas pipeline through the ultrasonic metering devices at thedifferent locations.

Step 220, determining any one of the at least one ultrasonic meteringdevice as a current metering device, and determining an accuracy of thecurrent metering device through verifying the current metering devicebased on the metering data of the current metering device and meteringdata of a related metering device.

The related metering device is a metering device provided in a pipelinerelated with the pipeline where the current metering device is located.

In some embodiments, the related metering device may include at leastone of an upper metering device, a lower metering device, and a parallelmetering device of the current metering device.

The upper metering device refers to an ultrasonic metering devicelocated in a pipeline upstream of the current metering device. The lowermetering device refers to an ultrasonic metering device located in apipeline downstream of the current metering device.

The parallel metering device refers to a metering device in the pipelinedownstream that belongs to a same level as the current metering deviceand has a same pipeline upstream. For example, a pipeline downstream ofa pipeline upstream X has three branches, including metering devices A,B, and C. Taking A as the current metering device, then B and C are theparallel metering devices of the current metering device A.

The accuracy is a degree to which the metering data of the meteringdevice matches an actual gas flow. The accuracy may be expressed as anumerical value as well as a preset level (e.g., level one to levelfive), the larger the numerical value and preset level, the greater theaccuracy.

In some embodiments, the smart gas device management platform maydetermine the accuracy of the current metering device in a variety ofways. Since metering data of adjacent upper metering devices is equal to(or approximately equal to) a sum of metering data of lower meteringdevices, the smart gas device management platform may verify themetering data of the current metering device with metering data of lowermetering devices corresponding to the current metering device todetermine the accuracy of the current metering device, and the smallerthe difference between the metering data of the current metering deviceand the sum of metering data of lower metering devices, the greater theaccuracy of the current metering device.

In some embodiments, the smart gas device management platform mayperform upstream verification using metering data of the upper meteringdevice, the metering data of the current metering device, and meteringdata of the parallel metering device during a same time period; andperform downstream verification using the metering data of the lowermetering device and the metering data of the current metering deviceduring the same time period. Further, the smart gas device managementplatform may determine the accuracy of the current metering device basedon an upstream verification result and a downstream verification result.

In some embodiments, the upstream verification may be to verify if thedifference between the metering data of the upper metering device andthe sum of the metering data of the current metering device and themetering data of the parallel metering device for the same time periodexceeds a verification error. If the difference exceeds the verificationerror, the verification fails.

In some embodiments, the downstream verification may be to verify if thedifference between the metering data of the current metering device andthe sum of the metering data of a plurality of lower metering devicesduring the same time period exceeds the verification error. If thedifference exceeds the verification error, the verification fails.

In some embodiments, the verification error may be related to gasdensity and gas pressure among the upper metering device, the parallelmetering device, and the current metering device. In some embodiments,the verification error may also be related to gas density and gaspressure between the lower metering device and the current meteringdevice. The verification error may be determined by an error evaluationmodel.

The verification error refers to a maximum value of an allowable errorin verifying the current metering data of the measuring device.

The gas density refers to data that characterizes density of gas in thegas pipeline.

The gas pressure refers to data that characterizes pressure of gas inthe gas pipeline.

The error evaluation model is a model to determine the verificationerror. In some embodiments, the error evaluation model may be a machinelearning model. For example, the error evaluation model may include aconvolutional neural network model, a neural network model, other custommodel structures, etc., or any combination thereof.

In some embodiments, an input of the error evaluation model may be thegas density and the gas pressure among the upper metering device, theparallel metering device, and the current metering device, or the gasdensity and the gas pressure between the lower metering device and thecurrent metering device.

In some embodiments, the input of the error evaluation model may be asequence. For example, the sequence may be [(a, b), (c, d), (e, f), (g,h)], wherein (a, b) is gas density a and gas pressure b of the pipelinewhere the current metering device is located, and (c, d), (e, f), and(g, h) are gas density and gas pressure from branch points of the gaspipeline where the three lower metering devices corresponding to thecurrent metering device are located to the respective lower meteringdevices. The branch points of the gas pipeline are intersections of thepipeline where the current metering device is located and the pipelineswhere the lower metering devices are located.

In some embodiments, an output of the error evaluation model may be theverification error of the current metering device.

In some embodiments, the error evaluation model may be trained based ona large number of first training samples with a first label. Each set oftraining samples of the first training samples may include gas densityand gas pressure among a sample upper metering device, a sample parallelmetering device, and a sample current metering device, or gas densityand gas pressure between a sample lower metering device and the samplecurrent metering device. The first label of the first training samplesmay be verification errors corresponding to the different trainingsamples. In some embodiments, the metering devices corresponding to thefirst training samples are devices whose accuracies are determined to bequalified after testing, and the first label may be obtained bycalculation based on the metering data of the metering devices in ahistorical record. For example, an error value of the metering databetween the metering devices corresponding to the first training sampleswhose accuracies are determined to be qualified after testing may bedetermined as the first label corresponding to the first trainingsamples.

The error evaluation model determines the verification error of thecurrent metering device based on the gas density and the gas pressurebetween the metering devices, improving the accuracy of the determinedverification error. It avoids the problems of data inconsistency betweenupstream and downstream metering devices and misjudging the accuracy ofthe metering device due to a variation of the gas density and the gaspressure in the pipeline.

The upstream verification result is a result of the verification of themetering data of the upper metering device with the metering data of thecurrent metering device and the parallel metering device. If adifference between the metering data of the upper metering device andthe metering data of the current metering device as well as the meteringdata of the parallel metering device of the current metering deviceexceeds the verification error, the upstream verification result fails.

The downstream verification result is a result of the verification ofthe metering data of the current metering device with the metering dataof the plurality of lower metering devices. If a difference between themetering data of the current metering device and the metering data ofthe plurality of lower metering devices exceeds the verification error,the downstream verification result fails.

In some embodiments, in response to both the upstream and downstreamverification results passing, the smart gas device management platformdetermines that the current metering device has a highest accuracy(e.g., level five), i.e., there are no problems with the currentmetering device.

In some embodiments, in response to both the upstream and downstreamverification results failing, the smart gas device management platformdetermines that the current metering device has a lowest accuracy (e.g.,level 1), i.e., there is a problem with the current metering device.

In some embodiments, in response to one of the upstream or thedownstream verification results failing, the smart gas device managementplatform may determine the accuracy of the current metering device basedon a corresponding manner described later.

Based on the upstream verification results and downstream verificationresults, the smart gas device management platform may morecomprehensively and accurately determine the accuracy of the currentmetering device, which helps to further improve the accuracy of adetermined adjustment instruction.

In some embodiments, in response to the upstream verification result andthe downstream verification result satisfying a preset condition, thesmart gas device management platform may verify at least one of theupper metering device, the lower metering device, and the parallelmetering device. Further, the smart gas device management platform maydetermine the accuracy of the current metering device based on theverification result of at least one of the upper metering device, thelower metering device, and the parallel metering device.

The preset condition may be that one of the upstream verification resultand the downstream verification result passes and one fails.

In some embodiments, in response to the upstream verification resultfailing, the smart gas device management platform may continue toperform the upstream verification on the upper metering device and thedownstream verification on the parallel metering device. Since the uppermetering device has already been verified with the lower meteringdevices (i.e., the upstream verification of the current metering devicehas already been performed), only the upstream verification of the uppermetering device is required. Since the parallel metering device hasalready been verified with the upper metering device and the currentmetering device (i.e., the upstream verification of the current meteringdevice has already been performed), only the downstream verification ofthe parallel metering device is required. In particular, the upstreamverification of the upstream metering device is similar to the upstreamverification of the current metering device, and the downstreamverification of the parallel metering device is similar to thedownstream verification of the current metering device, which may beseen in the relevant description above.

In some embodiments, in response to the upstream verification result ofthe upper metering device and the downstream verification result of theparallel metering device passing, the smart gas device managementplatform may consider the current metering device to be potentiallyproblematic and determine that the current metering device has thelowest accuracy (e.g., level 1).

In some embodiments, in response to the upstream verification result ofthe upper metering device and/or the downstream verification result ofthe parallel metering device failing, the smart gas device managementplatform may assume that there may be a problem with at least one of theupper metering device and the parallel metering device, while there maybe no problem with the current metering device, and that the currentmetering device has a high accuracy (e.g., level 4). When the upstreamverification result of the upper metering device fails, the smart gasdevice management platform may continue to perform the upstreamverification.

In some embodiments, a count of levels of upstream or downstreamverification may be determined by a count of verification steps. Forexample, if the verification is performed on the upper one levelmetering device of the current metering device, the verification step isone step up, and if the verification is performed on the upper twolevels metering device of the current metering device, the verificationstep is two steps up. More information about the count of verificationsteps may be found in the following section.

In some embodiments, in response to the verification result ofverification of the two steps up passing, the smart gas devicemanagement platform may assume that there may be no problem with theupper two levels metering device of the current metering device, andfurther determine that there may be a problem with the upper meteringdevice, then the current metering device has a high accuracy (e.g.,level four). In response to the verification result of verification oftwo steps up failing, the smart gas device management platform mayassume that there is no problem with the upper metering device of thecurrent metering device, and further determine that there may be aproblem with the current metering device and the current metering devicehas a moderate accuracy (e.g., level 3).

In some embodiments, in response to the downstream verification resultfailing, the smart gas device management platform may continue toperform the downstream verification on the lower metering device in asimilar manner as described above.

The accuracy of the determined accuracy of the current metering devicemay be improved by further verifying the upper, the lower, or theparallel metering device of the current metering devices to determinewhether the upper, the lower, or the parallel metering devices pass theverification and further determine the accuracy of the current meteringdevice.

In some embodiments, a count of verification steps for the uppermetering device or the lower metering device may be determined based ona current data upload frequency and a variation parameter of ahistorical accuracy.

The current data upload frequency is a frequency of data upload of theupper or the lower metering device at a current time.

The historical accuracy is an accuracy of the upper or the lowermetering device at a historical time.

The variation parameter is a parameter that characterizes a changebetween two adjacent historical accuracies, which may include amagnitude of the change, a direction of the change, a count of times ofthe historical accuracy below an accuracy threshold, etc. The accuracythreshold may be a preset minimum value of the accuracy of the meteringdevice. See FIG. 3 and its related description for more on the variationparameter of the historical accuracy.

In some embodiments, the current data upload frequency may be preset andobtained by the smart gas device management platform, and the variationparameter of the historical accuracy may be obtained through calculationby the smart gas device management platform based on a variation betweenthe two adjacent historical accuracies.

In some embodiments, the count of verification steps may be positivelycorrelated with the current data upload frequency and negativelycorrelated with the variation parameter of the historical accuracy. Forexample, the lower the current data upload frequency, the less attentiona data center pays to that metering device, the more likely it is thatthere is a problem with the upper metering device or the lower meteringdevice, and the fewer the verification steps. The smaller the magnitudeof change in the variation parameter of the historical accuracy and thefewer times the historical accuracy falls below the accuracy threshold,the less likely it is that there is a problem with the upper meteringdevice or the lower metering device. In view of this, it is necessary toverify the upper one level metering device (or the lower one levelmetering device) and increase the count of verification steps to makethe verification steps greater.

Determining the count of verification steps based on the current dataupload frequency and the variation parameter of the historical accuracymay further enable the determined accuracy of the current meteringdevice to be more accurate.

Step 230, sending an adjustment instruction to a target metering devicebased on the accuracy of at least one current metering devicecorresponding to the at least one ultrasonic metering device.

The target metering device is a metering device whose accuracy is belowthe accuracy threshold and may be problematic. More about the accuracythreshold may be found in the relevant description of step 220 above.

The smart gas device management platform may repeat step 220 todetermine the accuracies of all or part of the at least one ultrasonicmetering device.

The adjustment instruction is an instruction to make an adjustment tothe target metering device. For examples, the adjustment instructionincludes an instruction to deactivate the target metering device, a dataupload frequency instruction to adjust the target metering device (e.g.,increase the data upload frequency), a metering parameter instruction toadjust the target metering device, etc. The metering parameterinstruction to adjust the target metering device may include adjusting aprobe position, calibrating a sound velocity, performing reflectioncompensation, etc. More about the data upload frequency instruction andthe metering parameter instruction may be found in FIG. 3 and itsrelated contents.

The smart gas device management platform may determine the adjustmentinstruction in a variety of ways. For example, the smart gas devicemanagement platform determines an abnormality type of the targetmetering device based on the historical metering data of the targetmetering device, and determines the adjustment instructions based on theabnormality type.

More about determining the data upload frequency instruction and themetering parameter instruction may be found in FIG. 3 and its relatedcontents.

In some embodiments, the smart gas device management platform may sendthe adjustment instruction to the target metering device through thesmart gas sensing network platform based on the smart gas data center.

Through the method for remote control of the ultrasonic metering device,the target metering device may be adjusted more quickly to obtain moreaccurate metering data, and the metering device may be managed remotely,which improves management efficiency and realizes digitized andintelligent management.

FIG. 3 is an exemplary flowchart of determining an upload frequencyinstruction according to some embodiments of the present disclosure. Insome embodiments, a process 300 may be performed by a smart gas devicemanagement platform. As shown in FIG. 3 , the process 300 includes thefollowing steps.

In some embodiments, the adjustment instruction may include a dataupload frequency instruction. The data upload frequency instruction isused to adjust data upload frequency of a metering device. The dataupload frequency refers to a frequency at which the metering deviceuploads metering data to a smart gas sensing network platform within apreset time. The preset time may be a system default value or anartificially set value. The smart gas device management platform maydetermine the data upload frequency instruction by following steps310-330.

Step 310, determining a target metering device based on an accuracy.

More about the accuracy and the target metering device may be found inFIG. 2 and its related contents.

In some embodiments, an accuracy threshold of the metering device isrelated to a verification error. More about the verification errors maybe found in FIG. 2 and its related contents.

In some embodiments, the accuracy threshold is negatively correlatedwith the verification error. For example, when the verification error islarge, it indicates significant fluctuations in gas density and gaspressure in a gas pipeline, resulting in significant fluctuation in gasflow rate within the pipeline. Therefore, the corresponding accuracythreshold for the corresponding metering device may be adjusted to asmaller value.

By considering an impact of the verification error on the accuracythreshold, an impact of the fluctuations of the gas density and the gaspressure during gas transmission in the pipeline on the accuracy of themetering device is reduced.

Step 320, determining an abnormality type of the target metering devicebased on historical metering data of the target metering device.

The historical metering data refers to metering data within a certaintime range before a current time. The time range and the historicalmetering data may be system default values or artificially set values.

The abnormality type is a type of abnormal accuracy of the targetmetering device. The abnormality type may include a sporadicabnormality, a non-sporadic abnormality, etc. The sporadic abnormalityindicates that the accuracy of the target metering device sporadicallyfalls below the accuracy threshold. The non-sporadic abnormalityindicates that the accuracy of the target metering device frequentlyfalls below the accuracy threshold.

In some embodiments, the smart gas device management platform maydetermine the abnormality type in a variety of ways. For example, thesmart gas device management platform may analyze the historical meteringdata, and if a fluctuation of the historical metering data is greaterthan a fluctuation threshold, it indicates that the accuracy of themetering data is low due to fluctuation of a gas flow, and theabnormality type of the target metering device is determined to be thesporadic abnormality. On the contrary, if the fluctuation of thehistorical metering data is less than the fluctuation threshold, itindicates that the target metering device may be faulty, and theabnormality type of the target metering device is determined to be thenon-sporadic abnormality.

The fluctuation threshold may be a system default value or anartificially set value. The fluctuation of the historical metering datais an amount of change in gas flow data over a certain time period. Forexample, the fluctuation of the historical metering data may include astandard deviation of the gas flow data over the certain time period.

In some embodiments, the smart gas device management platform maydetermine a plurality of historical accuracies of a target meteringdevice based on the historical metering data. The smart gas devicemanagement platform may calculate variation parameters of the pluralityof historical accuracies, wherein the variation parameters include amagnitude and a direction of changes between adjacent historicalaccuracies, and a count of times of the historical accuracies below anaccuracy threshold. The smart gas device management platform maydetermine the abnormality type of the target metering device based onthe variation parameters and the current accuracy of the target meteringdevice.

More about the historical accuracy may be found in FIG. 2 and itsrelated contents.

In some embodiments, the smart gas device management platform maydetermine the historical accuracy based on the historical metering datain a similar manner to upstream verification and downstreamverification. More about the upstream verification and the downstreamverification may be found in FIG. 2 and its related contents.

The current accuracy is an accuracy at a current moment.

In some embodiments, when the current accuracy is lower than theaccuracy threshold, the smart gas device management platform may analyzethe historical accuracies, and in response to the magnitude of changebetween the historical accuracies is less than a magnitude of changethreshold, and the count of times of the historical accuracies below theaccuracy threshold is less than a count of times threshold, determinethe abnormality type to be the sporadic abnormality. Conversely, theabnormality type is most likely to be the non-sporadic abnormality.

The magnitude of change threshold and the count of times threshold maybe determined empirically or experimentally.

By calculating the variation parameters of the plurality of historicalaccuracies to determine the abnormality type of the target meteringdevice, more accurate results may be obtained compared to empiricaljudgment, which contributes to improving accuracy in determining thedata upload frequency instruction subsequently.

Step 330, determining the data upload frequency instruction based on theabnormality type and sending the data upload frequency instruction tothe target metering device.

In some embodiments, the smart gas device management platform maydetermine the data upload frequency instruction for the target meteringdevice in a variety of ways based on the abnormality type. For example,when the abnormality type is the sporadic abnormality, the data uploadfrequency of the target metering device is increased by a small amount,and the data upload frequency instruction is restored to the data uploadfrequency before the increase when the accuracy of the adjusted targetmetering device is monitored to be greater than the accuracy threshold.As another example, when the abnormality type is the non-sporadicabnormality, the data upload frequency of the target metering device isincreased by a large amount and the metering parameter of the targetmetering device is also adjusted, and the data upload frequencyinstruction is restored to the data upload frequency before the increasewhen the accuracy of the adjusted target metering device is monitored tobe greater than the accuracy threshold. More about adjusting themetering parameter of the target metering device may be found in FIG. 3and its related contents below. The small amount and the large amountmay be preset adjustment amounts.

By determining the abnormality type of the target metering device anddetermining the data upload frequency instruction, operating parametersof the target metering device may be adjusted in a targeted manner toimprove accuracy and reliability of data upload.

In some embodiments, the smart gas device management platform maydetermine the metering parameter instruction of the target meteringdevice based on steps S11-S13 as follows.

The metering parameters refer to parameters that affect the meteringaccuracy of the ultrasonic metering device. For example, the meteringparameters may include a probe position metering parameter, a soundvelocity calibration metering parameter, a reflection compensationmetering parameter, a signal processing metering parameter, atemperature compensation metering parameter, a pressure compensationmetering parameter, etc.

The probe position metering parameter refers to a parametercorresponding to a position of a probe of the ultrasonic metering devicewithin a fluid pipeline.

The sound velocity calibration metering parameter refers to a parametercorresponding to a velocity of ultrasonic waves propagating in a fluid.The velocity of ultrasonic waves propagating in the different fluidsvaries at different temperatures.

The reflection compensation parameter refers to a compensation parameterrelated with the reflection of ultrasonic waves when encounteringvarious obstacles (e.g., bubbles or sediments) in the fluid.

The signal processing metering parameter refers to a parameter relatedwith the processing of received signals by the ultrasonic meteringdevice. For example, the processing of signals includes filtering, gainadjustment, time delay, and other processing of the signals.

The temperature compensation metering parameter refers to a compensationparameter related with temperature changes.

The pressure compensation metering parameter refers to a compensationparameter related with pressure changes of fluid.

In some embodiments, the smart gas device management platform mayimprove the metering accuracy by adjusting the metering parameters ofthe target metering device.

The metering parameter instruction refers to an instruction to adjustthe metering parameters of the ultrasonic metering device. For example,the metering parameter instruction may include instructions foradjusting at least one of the probe position metering parameter, thesound velocity calibration metering parameter, the reflectioncompensation metering parameter, the signal processing meteringparameter, the temperature compensation metering parameter, the pressurecompensation metering parameter, etc.

Step S11, in response to the abnormality type being the non-sporadicabnormality, obtaining an error direction of the target metering device.

The error direction refers to a direction of deviation of the meteringdata of the target metering device compared to metering data of acorresponding metering device. The corresponding metering device may bean upper metering device or a plurality of lower metering devices orparallel metering devices. For example, the error direction may includerelatively large, relatively small, etc.

For example, in response to the metering data of the upper meteringdevice being greater than a sum of the metering data of the currentmetering device and the metering data of the parallel metering device ofthe current metering device, the smart gas device management platformdetermines the error direction as relatively small.

For example, in response to the metering data of the plurality of lowermetering devices being greater than the metering data of the currentmetering device, the smart gas device management platform determines theerror direction as relatively small.

Step S12, predicting suspicious abnormal parameter based on the errordirection, the historical metering data, pipeline parameters, andcurrent metering parameters of the target metering device.

More about the historical metering data may be found in FIG. 3 and itsrelated contents above.

The pipeline parameters are parameters related with the natural gastransmission pipeline itself. For example, the pipeline parameters mayinclude a material, an inner diameter, an outer diameter, etc., of thepipeline.

The current metering parameters refer to metering parameters of thetarget metering device at the current moment. More about the currentmetering parameters may be found in FIG. 4 and its related contents.

The suspicious abnormal parameter refers to one or more meteringparameters that may cause a degradation in the accuracy of the targetmetering device.

In some embodiments, the smart gas device management platform maydetermine the suspicious abnormal parameter in a variety of ways. Forexample, a smart gas device management platform may construct a featurevector based on the error direction, the historical metering data, thepipeline parameters, and the current metering parameters of the targetmetering device. The smart gas device management platform may search ina vector database based on the feature vector, determine a referencefeature vector that satisfies a preset matching condition as a relatedfeature vector, and determine a reference suspicious abnormal parametercorresponding to the related feature vector as the current suspiciousabnormal parameter. The preset matching condition refers to a judgmentcondition for determining the related feature vector. In someembodiments, the preset matching condition may include that a vectordistance is smaller than a distance threshold, the vector distance isthe smallest, or the like.

In some embodiments, the smart gas device management platform maypredict, based on a metering parameter analysis graph, abnormalprobabilities of a plurality of current metering parameters through aparameter analysis model, and thus determine the suspicious abnormalparameter, more of which may be found in the relevant description ofFIG. 4 and its related contents.

Step S13, determining the metering parameter instruction of the targetmetering device based on the suspicious abnormal parameter.

In some embodiments, the smart gas device management platform maydetermine the metering parameter instruction of the target meteringdevice in a variety of ways based on the suspicious abnormal parameter.The smart gas device management platform may preset a comparison tableamong the error direction, the suspicious abnormal parameter, and anadjusted metering parameter, determine the adjusted metering parameterby looking up the table, and determine the metering parameterinstruction of the target metering device based on the adjusted meteringparameter.

By predicting the suspicious abnormal parameter, it may avoidsimultaneous adjustment of all metering parameters of the targetmetering device, saving computational resources while improving theefficiency and accuracy of the adjustment.

In some embodiments, the metering parameter instructions may include aprobing parameter instruction and a target parameter instruction. Thesmart gas device management platform may perform a probing adjustment onthe target metering device based on the probing parameter instruction;and determine the target parameter instruction based on an adjustmenteffect of the target metering device.

The probing parameter instruction refers to an instruction to perform aprobing adjustment on the suspicious abnormal parameter of the targetmetering device. For example, the probing adjustment instruction mayinclude an adjusting direction, etc., and the adjusting direction mayinclude increasing or decreasing the suspicious abnormal parameter.

In some embodiments, the smart gas device management platform mayperform the probing adjustment on the target metering device (e.g.,increasing the suspicious abnormal parameter by a small amount) andre-evaluate the accuracy of the target metering device after theadjustment to determine changes in the accuracy of the target meteringdevice. If the accuracy is not increased or the accuracy decreases, thesmart gas device management platform may change the adjustment directionof the probing parameter instruction (e.g., decreasing the suspiciousabnormal parameter by a small amount) and perform the probing adjustmenton the target metering device again.

In some embodiments, the probing parameter instruction probes onesuspicious abnormal parameter at a time to accurately determine aneffective adjustment direction for each suspicious abnormal parameter.

The effective adjustment direction refers to a direction of adjustmentthat improves the accuracy.

In some embodiments, the smart gas device management platform may probeone suspicious abnormal parameter and re-evaluate the accuracy of thetarget metering device after adjustment to determine if the accuracy ofthe target metering device exceeds a first improvement magnitudethreshold. If the accuracy of the target metering device exceeds thefirst improvement magnitude threshold, the current adjustment directionis determined to be the effective adjustment direction. The firstimprovement magnitude threshold may be an artificially set value or asystem preset value.

Probing only one suspicious abnormal parameter at a time may improve thepertinence and accuracy of the adjustment, and help improve subsequentdetermination of the target parameter instruction. This avoids thesituation where the suspicious abnormal parameter that causes a changeof the accuracy may not be accurately located when performing theprobing adjustment on a plurality of suspicious abnormal parameters at asame time.

The adjustment effect of the target metering device may be animprovement magnitude of the accuracy of the target metering deviceafter the adjustment. The greater the improvement magnitude, the betterthe adjustment effect. Conversely, the smaller the improvementmagnitude, the worse the adjustment effect.

The target parameter instruction refers to an adjustment instruction forthe target metering parameter that enables the adjustment effect toachieve a desired target. The desired target may be an artificially setvalue.

In some embodiments, the smart gas device management platform maydetermine the target parameter instruction based on the adjustmenteffect (e.g., the improvement magnitude of the accuracy) of the targetmetering device. For example, a probing parameter instruction with alargest improvement magnitude among a plurality of probing parameterinstructions may be determined as the target parameter instruction.

Determining the target parameter instruction based on the adjustmenteffect of the target metering device corresponding to the probingparameter instruction may make the target parameter instruction moretargeted and accurate, and further improve the adjustment effect of thetarget parameter instruction.

FIG. 4 is an exemplary schematic diagram of determining a suspiciousabnormal parameter according to some embodiments of the presentdisclosure.

In some embodiments, the smart gas device management platform mayconstruct a metering parameter analysis graph 410 based on a gaspipeline network.

The metering parameter analysis graph refers to a graph that representsa relationship between individual ultrasonic metering devices. In someembodiments, the metering parameter analysis graph is a data structureconsisting of nodes and edges, where the edges connect the nodes, andthe nodes have features corresponding to the nodes and the edges havefeatures corresponding to the edges.

In some embodiments, there is the following situation that a deviationof the metering data of an individual metering device is within anallowable range, but there may be a large cumulative error when theupper and lower metering devices are considered together. Therefore, anoverall error of a plurality of metering devices needs to be consideredcomprehensively through the metering parameter analysis graph. Forexample, the metering parameters of the upper metering device are small,the metering parameters of the lower metering device are large, and themetering error caused by individual metering parameters is within theallowable range, but the cumulative metering error is large. Therefore,a complex relationship between the metering devices may be analyzed bythe metering parameter analysis graph, and the metering parameters ofeach metering device may be analyzed by taking the metering device witha high accuracy as a benchmark to determine abnormal metering parametersand adjust the abnormal metering parameters, so as to ensure that theoverall error of the plurality of metering devices in the pipeline meetsa requirement.

In some embodiments, the metering parameter analysis graph 410 includesa plurality of subgraphs, e.g., a subgraph 1, a subgraph n, etc. Thesubgraph 1 may include nodes of the subgraph 1, features of the nodes ofthe subgraph 1, edges of the subgraph 1, and features of the edges ofthe subgraph 1. The subgraph n may include nodes of the subgraph n,features of the nodes of the subgraph n, edges of the subgraph n, andfeatures of the edges of the subgraph n.

The features of the nodes of each subgraph reflect different currentmetering parameters. The current metering parameters differ fordifferent subgraphs. More about the current metering parameters may befound in the relevant description in FIG. 3 .

The nodes of the metering parameter analysis graph include ultrasonicmetering devices. The features of the nodes may reflect informationrelated with the ultrasonic metering devices. For example, the featuresof the node may include a model, accuracy, an error direction, and thecurrent metering parameters of the ultrasonic metering devices.

More about the error direction may be found in FIG. 3 and its relatedcontents. More about the ultrasonic metering devices and their accuracymay be found in FIG. 2 and its related contents.

The current metering parameters may be one kind of metering parameter ora combination of a plurality of metering parameters determined accordingto a physical correlation between different metering parameters or apreset rule. For example, in the case of limited volume, if the pressureof gas on a vessel wall increases, the temperature increases due to aconstant force area. Therefore, a temperature compensation meteringparameter and a pressure compensation metering parameter may bedetermined as a set of current metering parameters. As another example,considering that a sound speed is related to a temperature of fluid, thetemperature compensation metering parameter and a sound speedcalibration metering parameter may be determined as a set of currentmetering parameters. As shown in FIG. 4 , the features of the nodes inthe subgraph correspond to the set of current metering parameters.

In some embodiments, the smart gas device management platform may setthe error direction of a node whose accuracy is higher than a presetvalue as 0. The error direction of the nodes whose accuracy is lowerthan the preset value is set according to a result of manualverification, and the verification result is marked as positive if theerror direction is relatively large, and marked as negative if the errordirection is relatively small. The preset value may be a value set basedon experience or set manually. The content of the error direction beingrelatively large and relatively small may be seen in FIG. 3 .

The edges of the metering parameter analysis graph represent gaspipelines between the ultrasonic metering devices. For example, the edgeexists between two nodes directly connected by the gas pipeline. Thefeatures of the edges may reflect information related to the gaspipeline and the fluid transported in the gas pipeline. For example, thefeatures of the edges may include a length of the pipeline, density ofthe gas in the pipeline, gas pressure, a gas temperature, etc. Moreabout the gas density, the gas pressure, and the gas temperature may befound in FIG. 2 and its related contents.

The features of the nodes and the features of the edges may bedetermined in various ways based on underlying data. A source of thedata may be the ways illustrated in other embodiments or other ways. Thedata may include current data, historical data, etc. For example, datasuch as the gas density, the gas pressure, and the gas temperature maybe obtained based on sensors in the pipeline. The smart gas devicemanagement platform may compile a sequence of data from a plurality ofsensors, and the sequence may include a sensor type, a sensor location,a sensor reading, etc.

In some embodiments, the smart gas device management platform maypredict abnormal probabilities 430 of the set of current meteringparameters (e.g., abnormal probability 430-1, abnormal probability430-n, etc.) of the features of the nodes of different subgraphsrespectively based on the metering parameter analysis graph 410 (aplurality of subgraphs) through a plurality of parameter analysissublayers of a parameter analysis model 420. The abnormal probabilities430 may include a probability for each current metering parameter of theset of current metering parameters.

In some embodiments, the parameter analysis model may be a Graph NeuralNetwork (GNN) model, or other graph models, such as a GraphConvolutional Neural Network (GCNN) model, or a graph neural networkmodel with other processing layers, modified processing techniques, etc.

In some embodiments, the plurality of parameter analysis sublayers mayinclude parameter analysis sublayer 1, parameter analysis sublayer n,etc.

In some embodiments, the abnormal probabilities of the set of currentmetering parameters output by the parameter analysis sublayerscorrespond to nodes of ultrasonic metering devices with accuracies lowerthan an accuracy threshold and a cause of the abnormality of theultrasonic metering device is non-sporadic. The smart gas devicemanagement platform may mark in advance nodes that need to be output andnodes that do not need to be output respectively in the meteringparameter analysis graph 410.

In some embodiments, the smart gas device management platform maydetermine the suspicious parameters based on the abnormal probabilitiesof a plurality of sets of current metering parameters. For example, thesmart gas device management platform may determine current meteringparameters whose abnormal probabilities are higher than a probabilitythreshold as the suspicious abnormal parameter 440. The probabilitythreshold may be a system preset value or artificially set value.

In some embodiments, the parameter analysis model may be obtained basedon training data. The training data includes second training samples andsecond labels. For example, the second training samples may include ahistorical parameter analysis graph, and each historical parameteranalysis graph may include a plurality of historical subgraphs. Eachsecond training sample is labeled with corresponding preset nodes. Thesecond label indicates whether the current metering parameterscorresponding to the preset nodes in each historical subgraph areactually abnormal (a label of 1 indicates abnormal, and a label of 0indicates non-abnormal). The second training samples may be determinedbased on historical data and the second labels may be determined by thesmart gas device management platform or human labeling. The preset nodesmay be nodes for which the accuracy of the corresponding ultrasonicmetering devices is below the accuracy threshold.

By utilizing the parameter analysis graph and the complex physicalcorrelation, the parameter analysis model may accurately predict theabnormal probabilities of the current metering parameters, allowing foraccurate determination of the suspicious abnormal parameter and energysavings.

One or more embodiments of the present disclosure also provide anon-transitory, computer-readable storage medium, wherein the storagemedium stores computer instructions, and when a computer reads thecomputer instructions in the storage medium, the computer runs themethod for remote control of the ultrasonic metering device as describedin the embodiments of the present disclosure.

The basic concepts have been described above, and it is clear that theabove detailed disclosure is intended as an example only for thoseskilled in the art and does not constitute a limitation of the presentdisclosure. Although not explicitly stated herein, there are variousmodifications, improvements, and amendments that may be made to thepresent disclosure by those skilled in the art. Such modifications,improvements, and amendments are suggested in the present disclosure, sosuch modifications, improvements, and amendments remain within thespirit and scope of the exemplary embodiments of the present disclosure.

Also, the present disclosure uses specific words to describe embodimentsof the present disclosure. For example, “one embodiment,” “anembodiment,” and/or “some embodiments” mean that a certain feature,structure, or characteristic is connected with at least one embodimentof the present disclosure. Therefore, it should be emphasized and notedthat “one embodiment” or “an embodiment” or “an alternative embodiment”mentioned twice or more in different places in the present disclosuredoes not necessarily refer to the same embodiment. In addition, certainfeatures, structures, or characteristics of one or more embodiments ofthe present disclosure may be suitably combined.

In closing, it is to be understood that the embodiments of the presentdisclosure disclosed herein are illustrative of the principles of theembodiments of the present disclosure. Other variations may also fallwithin the scope of the present disclosure. Therefore, merely by way ofexample and not limitation, alternative configurations of theembodiments of the present disclosure may be considered consistent withthe teachings of the present disclosure. Accordingly, the embodiments ofthe present disclosure are not limited to the embodiments explicitlyintroduced and described in the present disclosure.

What is claimed is:
 1. A method for remote control of an ultrasonicmetering device, wherein the method is implemented by a smart gas devicemanagement platform of an Internet of Things (IoT) system for remotecontrol of the ultrasonic metering device, and the method comprises:obtaining metering data of at least one ultrasonic metering device;determining any one of the at least one ultrasonic metering device as acurrent metering device; determining an accuracy of the current meteringdevice through verifying the current metering device based on meteringdata of the current metering device and metering data of a relatedmetering device, wherein the related metering device includes at leastone of an upper metering device, a lower metering device, and a parallelmetering device of the current metering device; and sending anadjustment instruction to a target metering device based on the accuracyof at least one current metering device corresponding to the at leastone ultrasonic metering device.
 2. The method according to claim 1,wherein the determining an accuracy of the current metering devicethrough verifying the current metering device based on the metering dataof the current metering device and metering data of a related meteringdevice comprises: performing upstream verification using metering dataof the upper metering device, metering data of the current meteringdevice, and metering data of the parallel metering device during a sametime period; performing downstream verification using metering data ofthe lower metering device and the metering data of the current meteringdevice during the same time period; and determining the accuracy of thecurrent metering device based on an upstream verification result and adownstream verification result.
 3. The method according to claim 2,wherein the determining the accuracy of the current metering devicebased on an upstream verification result and a downstream verificationresult includes: in response to the upstream verification result and thedownstream verification result satisfying a preset condition, performingverification on the at least one of the upper metering device, the lowermetering device, or the parallel metering device; and determining theaccuracy of the current metering device based on a verification resultof the at least one of the upper metering device, the lower meteringdevice, or the parallel metering device.
 4. The method according toclaim 3, wherein a count of verification steps of the upper meteringdevice or the lower metering device is determined by a processincluding: determining the count of verification steps based on acurrent data upload frequency and a variation parameter of a historicalaccuracy of the upper metering device or the lower metering device. 5.The method according to claim 1, wherein the adjustment instructionincludes a data upload frequency instruction, and the sending theadjustment instruction to a target metering device based on the accuracyof at least one current metering device corresponding to the at leastone ultrasonic metering device includes: determining the target meteringdevice based on the accuracy, wherein an accuracy of the target meteringdevice is lower than an accuracy threshold; determining an abnormalitytype of the target metering device based on historical metering data ofthe target metering device; and determining the data upload frequencyinstruction based on the abnormality type and sending the data uploadfrequency instruction to the target metering device.
 6. The methodaccording to claim 5, wherein the determining an abnormality type of thetarget metering device based on historical metering data of the targetmetering device includes: determining a plurality of historicalaccuracies of the target metering device based on the historicalmetering data; counting variation parameters of the plurality ofhistorical accuracies, wherein the variation parameters include amagnitude and a direction of changes between adjacent historicalaccuracies, and a count of times of the historical accuracies below theaccuracy threshold; and determining the abnormality type of the targetmetering device based on the variation parameters and a current accuracyof the target metering device.
 7. The method according to claim 5,wherein the accuracy threshold is related to a verification error. 8.The method according to claim 5, wherein the adjustment instructionfurther includes a metering parameter instruction, and the meteringparameter instruction is determined based on operations including: inresponse to the abnormality type being a non-sporadic abnormality,obtaining an error direction of the target metering device; predicting asuspicious abnormal parameter based on the error direction, thehistorical metering data, pipeline parameters, and current meteringparameters of the target metering device; and determining the meteringparameter instruction of the target metering device based on thesuspicious abnormal parameter.
 9. The method according to claim 8,wherein the predicting a suspicious abnormal parameter based on theerror direction, the historical metering data, pipeline parameters, andcurrent metering parameters of the target metering device includes:constructing a metering parameter analysis graph based on a gas pipelinenetwork, wherein the metering parameter analysis graph includes aplurality of subgraphs, and nodes of the metering parameter analysisgraph include the ultrasonic metering devices, and features of the nodesinclude models of the ultrasonic metering devices, the accuracy, theerror direction, and the current metering parameters, the plurality ofsubgraphs having different current metering parameters, and edges of themetering parameter analysis graph represent gas pipelines between theultrasonic metering devices, and features of the edges include pipelinelengths, gas density in the pipelines, gas pressure, and gastemperatures; predicting, based on the metering parameter analysisgraph, abnormal probabilities of a plurality of current meteringparameters through a parameter analysis model, the parameter analysismodel being a machine learning model; and determining the suspiciousabnormal parameter based on the abnormal probabilities.
 10. The methodaccording to claim 8, wherein the metering parameter instructionincludes a probing parameter instruction and a target parameterinstruction, and the determining the metering parameter instruction ofthe target metering device based on the suspicious abnormal parameterincludes: performing a probing adjustment on the target metering devicebased on the probing parameter instruction; and determining the targetparameter instruction based on an adjustment effect of the targetmetering device.
 11. An Internet of Things (IoT) system for remotecontrol of an ultrasonic metering device, wherein the IoT systemincludes a smart gas user platform, a smart gas service platform, asmart gas device management platform, a smart gas sensing networkplatform, and a smart gas object platform; the smart gas user platformincludes a plurality of smart gas user sub-platforms; the smart gasservice platform includes a plurality of smart gas servicesub-platforms; the smart gas device management platform includes aplurality of smart gas device management sub-platforms and a smart gasdata center, the smart gas device management platform being configuredto transmit an adjustment instruction to the smart gas sensing networkplatform via the smart gas data center; the smart gas sensing networkplatform is configured to interact with the smart gas data center andthe smart gas object platform and send the adjustment instruction to thesmart gas object platform; the smart gas object platform is configuredto obtain metering data of at least one ultrasonic metering device; thesmart gas device management platform is configured to: determine any oneof the at least one ultrasonic metering device as a current meteringdevice; determine an accuracy of the current metering device throughverifying the current metering device based on metering data of thecurrent metering device and metering data of a related metering device,wherein the related metering device includes at least one of an uppermetering device, a lower metering device, and a parallel metering deviceof the current metering device; and send the adjustment instruction to atarget metering device based on the accuracy of at least one currentmetering device corresponding to the at least one ultrasonic meteringdevice.
 12. The IoT system according to claim 11, wherein the smart gasdevice management platform is further configured to: perform upstreamverification using metering data of the upper metering device, meteringdata of the current metering device, and metering data of the parallelmetering device during a same time period; perform downstreamverification using metering data of the lower metering device and themetering data of the current metering device during the same timeperiod; and determine the accuracy of the current metering device basedon an upstream verification result and a downstream verification result.13. The IoT system according to claim 12, wherein the smart gas devicemanagement platform is further configured to: in response to theupstream verification result and the downstream verification resultsatisfying a preset condition, perform verification on at least one ofthe upper metering device, the lower metering device, or the parallelmetering device; and determine the accuracy of the current meteringdevice based on a verification result of the at least one of the uppermetering device, the lower metering device, or the parallel meteringdevice.
 14. The IoT system according to claim 12, wherein the smart gasdevice management platform is further configured to: determine a countof verification steps based on a current data upload frequency and avariation parameter of a historical accuracy of the upper meteringdevice or the lower metering device.
 15. The IoT system according toclaim 11, wherein the adjustment instruction includes a data uploadfrequency instruction, and the smart gas device management platform isfurther configured to: determine the target metering device based on theaccuracy, wherein an accuracy of the target metering device is lowerthan an accuracy threshold; determine an abnormality type of the targetmetering device based on historical metering data of the target meteringdevice; and determine the data upload frequency instruction based on theabnormality type and send the data upload frequency instruction to thetarget metering device.
 16. The IoT system according to claim 15,wherein the gas device management platform is further configured to:determine a plurality of historical accuracies of the target meteringdevice based on the historical metering data; counting variationparameters of the plurality of historical accuracies, wherein thevariation parameters include a magnitude and a direction of changesbetween adjacent historical accuracies, and a count of times of thehistorical accuracies below the accuracy threshold; and determine theabnormality type of the target metering device based on the variationparameters and the current accuracy of the target metering device. 17.The IoT system according to claim 15, wherein the adjustment instructionfurther includes a metering parameter instruction, and the smart gasdevice management platform is further configured to: in response to theabnormality type being a non-sporadic abnormality, obtain an errordirection of the target metering device; predict a suspicious abnormalparameter based on the error direction, the historical metering data,pipeline parameters, and current metering parameters of the targetmetering device; and determine the metering parameter instruction of thetarget metering device based on the suspicious abnormal parameter. 18.The IoT system according to claim 17, wherein the smart gas devicemanagement platform is further configured to: construct a meteringparameter analysis graph based on a gas pipeline network, wherein themetering parameter analysis graph includes a plurality of subgraphs, andnodes of the metering parameter analysis graph include the ultrasonicmetering devices, and features of the nodes include models of theultrasonic metering devices, the accuracy, the error direction, and thecurrent metering parameters, the plurality of subgraphs having differentcurrent metering parameters, and edges of the metering parameteranalysis graph represent gas pipelines between the ultrasonic meteringdevices, and features of the edges include pipeline lengths, gas densityin the pipelines, gas pressure, and gas temperatures; predict, based onthe metering parameter analysis graph, abnormal probabilities of aplurality of current metering parameters through a parameter analysismodel, the parameter analysis model being a machine learning model; anddetermine the suspicious abnormal parameter based on the abnormalprobabilities.
 19. The IoT system according to claim 17, wherein themetering parameter instruction includes a probing parameter instructionand a target parameter instruction, and the smart gas device managementplatform is further configured to: perform a probing adjustment on thetarget metering device based on the probing parameter instruction; anddetermine the target parameter instruction based on an adjustment effectof the target metering device.
 20. A non-transitory computer-readablestorage medium, wherein the storage medium stores computer instructions,and when a computer reads the computer instructions in the storagemedium, the computer implements the method of claim 1.